Tolerance of Ambiguity in Veterinary Students at the Beginning and End of a Second-year Clinical Pathology Course
Bibliographic record
Abstract
Tolerance of ambiguity (TOA) is essential for veterinarians because ambiguity and uncertainty are unavoidable aspects of veterinary practice. However, TOA has been little investigated in veterinarians or veterinary students. In this article, the 27-item Tolerance of Ambiguity of Veterinary Students (TAVS) scale, including eight additional clinical pathology-specific items, is used to evaluate TOA in veterinary students at the beginning and end of a clinical pathology course. Clinical pathology is often one of the first subjects in which students encounter ambiguity because real-life cases are used in teaching. The hypotheses are that TOA will increase across the course and that TOA will correlate with the final grade in the course. Analysis of the TAVS scale revealed very good inter-item reliability (α = 0.80) and a positive correlation between the original TAVS items and the new clinical pathology items (ρ = 0.63). Students demonstrated a significant increase in TOA across the course for TAVS items and a similar trend for clinical pathology items. Four items related to affinity for complexity and novice view showed significant increases in TOA. Two items related to discomfort from uncertainty showed significant decreases. There was no correlation between TOA and final grade in the course. Students rated their personal frustration with ambiguity in the course as low and did not think ambiguity in cases was problematic for teaching. The results suggest that the increased TOA at the end of the course might relate to students being taught-and learning how to cope with-ambiguity through the real-life cases used for teaching.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".